EAGER: Collaborative Research: Learning Relations between Extreme Weather Events and Planet-Wide Environmental Trends

极端事件，如热浪、寒潮、极端降水和严重风暴，在生命损失和生态系统和基础设施破坏方面发挥着重要作用，对可持续性提出了根本性挑战。在全球环境趋势的预期趋势下，极端事件的强度、持续时间和频率的预估变化存在相当大的不确定性。减少这些不确定性是一项巨大的挑战，需要在环境和数据科学方面取得实质性进展。拟议的研究旨在推进支持极端事件预测的环境科学，以及识别大量数据集中变量之间关系所需的数据科学。这项研究的结果将为改进极端事件的预测提供基础，以用于可持续性规划。该项目将教育和交叉训练两个学科的研究生，使他们能够为这个新兴领域做出贡献。拟议的研究还将为课程开发提供信息，并将通过教程、会议和研讨会进行传播。该团队与研讨会、乔治华盛顿大学可持续发展研究所和乔治华盛顿大学地球前进计划的合作将有助于通过公众宣传扩大影响。提出的研究将通过以下方式推进大规模和区域事件的机器学习和统计建模：(1)在高维稀疏回归中使用新工具；(2)识别数据中的非线性关系；(3)时空数据中随空间和时间变化的非平稳学习关系。这项研究的结果将促进对极端天气事件及其与全球环境趋势的关系的理解。这种关系将通过应用新的统计算法来分析产生非常大数据集的广泛气候模式模拟来学习。研究结果将根据观察结果进行验证，并将不同模型之间的学习关系进行比较，以评估一致性和稳健性，并验证模型。

Extreme events, such as heat waves, cold spells, extreme precipitation, and severe storms, play a significant role in the loss of lives and damage to ecosystems and infrastructure, presenting fundamental challenges to sustainability. Under anticipated trends in planet-scale environmental trends, there is considerable uncertainty in the projected changes in the intensity, duration, and frequency of extreme events. Reducing these uncertainties is a grand challenge that will require substantial advances in both the environmental and data sciences. The proposed research seeks to advance both the environmental science that underpins predictions of extreme events, and the data science required to identify relations between variables in massive data sets. The results of this research will provide a basis for improving predictions of extreme events for use in sustainability planning. This project will educate and cross-train graduate students in both disciplines, allowing them to contribute to this new emerging field. The proposed research will also inform course development, and will be disseminated through tutorials, conferences, and seminars. The team's involvement with workshops, the GW Sustainability Institute, and GW Planet Forward will help to broaden the impact through public outreach.The proposed research will advance machine learning and statistical modeling of large-scale and regional events by: (1) using new tools in sparse regression in high dimensions, (2) identifying nonlinear relations in data, and (3) learning relations in spatiotemporal data that are non-stationary over space and time. The results of this research will advance understanding of extreme weather events and their relation to planet-wide environmental trends. Such relations will be learned by applying new statistical algorithms to analyze extensive climate model simulations which generate very large data sets. The findings will be validated against observations, and the learned relations will be compared between different models to assess consistency and robustness, and to validate models.